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On asymptotically distribution free tests with parametric hypothesis for ergodic diffusion processes

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  • M. Kleptsyna
  • Yu. Kutoyants

Abstract

We consider the problem of the construction of the asymptotically distribution free test by the observations of ergodic diffusion process. It is supposed that under the basic hypothesis the trend coefficient depends on a finite-dimensional parameter and we study the Cramér-von Mises type statistics. The underlying statistics depends on the deviation of the local time estimator from the invariant density with parameter replaced by the maximum likelihood estimator. We propose a linear transformation which yields the convergence of the test statistics to an integral of the Wiener process. Therefore the test based on this statistics is asymptotically distribution free. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • M. Kleptsyna & Yu. Kutoyants, 2014. "On asymptotically distribution free tests with parametric hypothesis for ergodic diffusion processes," Statistical Inference for Stochastic Processes, Springer, vol. 17(3), pages 295-319, October.
    • Handle: RePEc:spr:sistpr:v:17:y:2014:i:3:p:295-319
    DOI: 10.1007/s11203-014-9096-3
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    References listed on IDEAS

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    1. Ilia Negri & Yoichi Nishiyama, 2009. "Goodness of fit test for ergodic diffusion processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(4), pages 919-928, December.
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    Cited by:

    1. Maroua Ben Abdeddaiem, 2016. "On goodness-of-fit tests for parametric hypotheses in perturbed dynamical systems using a minimum distance estimator," Statistical Inference for Stochastic Processes, Springer, vol. 19(3), pages 259-287, October.
    2. Dabye, A.S. & Kutoyants, Yu.A. & Tanguep, E.D., 2019. "On APF test for Poisson process with shift and scale parameters," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 28-36.
    3. Estate V. Khmaladze, 2021. "How to test that a given process is an Ornstein–Uhlenbeck process," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 405-419, July.

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